Run splicing cluster assignment, splicing burden, gsva, CLK1
Ryan Corbett, adapted by Jo Lynne Rokita for CLK1
2024
Purpose:
Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) 2) KEGG Spliceosome GSVA scores or 3) CLK1 exon 4 TPM as a predictor
Usage
Uses a wrapper function (survival_analysis) from utils
folder.
Setup
Packages and functions
Load packages, set directory paths and call setup script
library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
data_dir <- file.path(root_dir, "data")
analysis_dir <- file.path(root_dir, "analyses", "survival")
input_dir <- file.path(analysis_dir, "results")
results_dir <- file.path(analysis_dir, "results")
plot_dir <- file.path(analysis_dir, "plots")
# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
dir.create(results_dir, recursive = TRUE)
}
source(file.path(analysis_dir, "util", "survival_models.R"))Set metadata and cluster assignment file paths
metadata_file <- file.path(input_dir, "splicing_indices_with_survival.tsv")
cluster_file <- file.path(root_dir, "analyses",
"sample-psi-clustering", "results",
"sample-cluster-metadata-top-5000-events-stranded.tsv")
kegg_scores_stranded_file <- file.path(root_dir, "analyses",
"sample-psi-clustering", "results",
"gsva_output_stranded.tsv")
tpm_file <- file.path(data_dir, "rna-isoform-expression-rsem-tpm.rds")
clk1_psi_file <- file.path(root_dir,
"analyses",
"CLK1-splicing_correlations",
"results",
"clk1-exon4-psi.tsv")Wrangle data Add cluster assignment and spliceosome gsva scores to
metadata and define column lgg_group (LGG or
non_LGG)
metadata <- read_tsv(metadata_file)Rows: 684 Columns: 26
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (11): Kids_First_Biospecimen_ID, Histology, Kids_First_Participant_ID, molecular_subtype, extent_of_tumor_resection, EFS_event_type, OS_statu...
dbl (15): Total, AS_neg, AS_pos, AS_total, SI_A3SS, SI_A5SS, SI_RI, SI_SE, SI_Total, EFS_days, OS_days, age_at_diagnosis_days, age_at_diagnosis, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.clusters <- read_tsv(cluster_file) %>%
dplyr::rename(Kids_First_Biospecimen_ID = sample_id)Rows: 729 Columns: 8
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (6): sample_id, plot_group, plot_group_hex, RNA_library, molecular_subtype, plot_group_n
dbl (2): cluster, group_n
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.clk1_psi <- read_tsv(clk1_psi_file) %>%
dplyr::rename(CLK1_ex4_PSI = PSI) %>%
select(-plot_group)Rows: 729 Columns: 3
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): Kids_First_Biospecimen_ID, plot_group
dbl (1): PSI
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
dplyr::filter(geneset == "KEGG_SPLICEOSOME") %>%
dplyr::rename(spliceosome_gsva_score = score)Rows: 22599 Columns: 3
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): sample_id, geneset
dbl (1): score
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.all_clk4_transcr_counts <- readRDS(tpm_file) %>%
filter(grepl("^CLK1", gene_symbol)) %>%
mutate(
transcript_id = case_when(
transcript_id %in% c("ENST00000321356.9", "ENST00000434813.3", "ENST00000409403.6") ~ "Exon 4",
# transcript_id == "ENST00000321356.9" ~ "Exon 4",
TRUE ~ "Other"
)
) %>%
group_by(transcript_id) %>%
summarise(across(starts_with("BS"), sum, na.rm = TRUE)) %>%
pivot_longer(cols = -transcript_id, names_to = "Kids_First_Biospecimen_ID", values_to = "CLK1_Ex4_TPM") %>%
filter(transcript_id == "Exon 4") %>%
inner_join(clusters, by = "Kids_First_Biospecimen_ID") %>%
left_join(clk1_psi)Joining with `by = join_by(Kids_First_Biospecimen_ID)`# how many clusters?
n_clust <- length(unique(clusters$cluster))
metadata <- metadata %>%
right_join(all_clk4_transcr_counts %>% dplyr::select(Kids_First_Biospecimen_ID,
cluster, CLK1_Ex4_TPM, CLK1_ex4_PSI)) %>%
left_join(gsva_scores %>% dplyr::select(sample_id,
spliceosome_gsva_score),
by = c("Kids_First_Biospecimen_ID" = "sample_id")) %>%
dplyr::mutate(cluster = glue::glue("Cluster {cluster}")) %>%
dplyr::mutate(cluster = fct_relevel(cluster,
paste0("Cluster ", 1:n_clust))) %>%
dplyr::mutate(lgg_group = case_when(
plot_group == "Low-grade glioma" ~ "LGG",
TRUE ~ "non-LGG"
)) %>%
dplyr::mutate(SBI = SI_Total * 10) %>%
dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)Joining with `by = join_by(Kids_First_Biospecimen_ID)`Generate coxph models including extent of tumor resection, lgg group, cluster assignment, SBI, and CLK1 exon 4 TPM as covariates
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
forest_os
ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
forest_os,
width = 10, height = 6, units = "in",
device = "pdf")
add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
forest_efs
ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
repeat analysis with CLK1 exon 4 TPM alone
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
forest_os
ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
forest_os,
width = 10, height = 6, units = "in",
device = "pdf")
add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
forest_efs
ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
NA
NArepeat analysis with CLK1 exon 4 PSI
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
forest_os
ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
forest_os,
width = 10, height = 6, units = "in",
device = "pdf")
add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
forest_efs
ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
Interaction with GSVA, SBI, CLK1
models <- c("spliceosome_gsva_score", "SBI", "CLK1_Ex4_TPM", "CLK1_ex4_PSI")
# by cluster
for (each in models) {
int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
int_forest_efs
ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
int_forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
int_forest_os
ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
int_forest_os,
width = 10, height = 6, units = "in",
device = "pdf")
}
## clk1 x age
int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS"))))
int_forest_efs
ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_clk1_age.pdf")),
int_forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS")),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS"))))
int_forest_os
ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_clk1_age.pdf")),
int_forest_os,
width = 10, height = 6, units = "in",
device = "pdf")
models2 <- c("SBI", "CLK1_Ex4_TPM")
for (each in models2) {
#### by spliceosome_gsva_score
int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = paste0("extent_of_tumor_resection+lgg_group+cluster+spliceosome_gsva_score*", each, "+age_at_diagnosis_years"),
file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS"))))
int_forest_efs
ggsave(file.path(plot_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".pdf")),
int_forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
}
add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))
forest_efs
ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))
forest_os
ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
forest_efs,
width = 10, height = 6, units = "in",
device = "pdf")
Filter for clusters
cluster_list <- unique(metadata$cluster)
for (each in cluster_list) {
cluster_df <- metadata %>%
dplyr::filter(cluster == each,
!is.na(EFS_days)) %>%
dplyr::mutate(CLK1_TPM_group = case_when(
CLK1_Ex4_TPM > summary(CLK1_Ex4_TPM)["3rd Qu."] ~ "High CLK1 TPM",
CLK1_Ex4_TPM < summary(CLK1_Ex4_TPM)["1st Qu."] ~ "Low CLK1 TPM",
TRUE ~ NA_character_),
CLK1_PSI_group = case_when(CLK1_ex4_PSI > summary(CLK1_ex4_PSI)["3rd Qu."] ~ "High CLK1 PSI",
CLK1_ex4_PSI < summary(CLK1_ex4_PSI)["1st Qu."] ~ "Low CLK1 PSI",
TRUE ~ NA_character_
)) %>%
dplyr::mutate(CLK1_TPM_group = fct_relevel(CLK1_TPM_group,
c("Low CLK1 TPM", "High CLK1 TPM")),
CLK1_PSI_group = fct_relevel(CLK1_PSI_group,
c("Low CLK1 PSI", "High CLK1 PSI")))
safe_each <- gsub("[^A-Za-z0-9_-]+", "_", each)
# Generate KM models with `CLK1_TPM_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_tpm_kap_os <- survival_analysis(
metadata = cluster_df %>%
dplyr::filter(!is.na(CLK1_TPM_group)),
ind_var = "CLK1_TPM_group",
test = "kap.meier",
metadata_sample_col = "Kids_First_Biospecimen_ID",
days_col = "OS_days",
status_col = "OS_status"
)
readr::write_rds(cluster_clk_tpm_kap_os,
file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_tpm_group.RDS")))
cluster_clk_tpm_kap_efs <- survival_analysis(
metadata = cluster_df %>%
dplyr::filter(!is.na(CLK1_TPM_group)),
ind_var = "CLK1_TPM_group",
test = "kap.meier",
metadata_sample_col = "Kids_First_Biospecimen_ID",
days_col = "EFS_days",
status_col = "EFS_status"
)
readr::write_rds(cluster_clk_tpm_kap_efs,
file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_tpm_group.RDS")))
# Generate KM models with `CLK1_PSI_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_psi_kap_os <- survival_analysis(
metadata = cluster_df %>%
dplyr::filter(!is.na(CLK1_PSI_group)),
ind_var = "CLK1_PSI_group",
test = "kap.meier",
metadata_sample_col = "Kids_First_Biospecimen_ID",
days_col = "OS_days",
status_col = "OS_status"
)
readr::write_rds(cluster_clk_psi_kap_os,
file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_psi_group.RDS")))
cluster_clk_psi_kap_efs <- survival_analysis(
metadata = cluster_df %>%
dplyr::filter(!is.na(CLK1_PSI_group)),
ind_var = "CLK1_PSI_group",
test = "kap.meier",
metadata_sample_col = "Kids_First_Biospecimen_ID",
days_col = "EFS_days",
status_col = "EFS_status"
)
readr::write_rds(cluster_clk_psi_kap_efs,
file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_psi_group.RDS")))
# Generate cluster KM SI_group plots
km_cluster_clk_tpm_os_plot <- plotKM(model = cluster_clk_tpm_kap_os,
variable = "CLK1_TPM_group",
combined = F,
title = paste0(each, ", overall survival"),
p_pos = "topright")
ggsave(file.path(plot_dir, paste0("km_", each, "_OS_clk1_tpm_group.pdf")),
km_cluster_clk_tpm_os_plot,
width = 8, height = 5, units = "in",
device = "pdf")
km_cluster_clk1_tpm_efs_plot <- plotKM(model = cluster_clk_tpm_kap_efs,
variable = "CLK1_TPM_group",
combined = F,
title = paste0(each, ", event-free survival"),
p_pos = "topright")
ggsave(file.path(plot_dir, paste0( "km_", each, "_EFS_clk1_tpm_group.pdf")),
km_cluster_clk1_tpm_efs_plot,
width = 8, height = 5, units = "in",
device = "pdf")
km_cluster_clk1_psi_os_plot <- plotKM(model = cluster_clk_psi_kap_os,
variable = "CLK1_PSI_group",
combined = F,
title = paste0(each, ", overall survival"),
p_pos = "topright")
ggsave(file.path(plot_dir, paste0( "km_", each, "_OS_clk1_psi_group.pdf")),
km_cluster_clk1_psi_os_plot,
width = 8, height = 5, units = "in",
device = "pdf")
km_cluster_clk1_psi_efs_plot <- plotKM(model = cluster_clk_psi_kap_efs,
variable = "CLK1_PSI_group",
combined = F,
title = paste0(each, ", event-free survival"),
p_pos = "topright")
ggsave(file.path(plot_dir, paste0("km_", each, "_EFS_clk1_psi_group.pdf")),
km_cluster_clk1_psi_efs_plot,
width = 8, height = 5, units = "in",
device = "pdf")
# Assess EFS and OS by CLK1 TPM group in multivariate models and generate forest plots
add_model_cluster_efs <- fit_save_model(cluster_df %>%
dplyr::filter(extent_of_tumor_resection != "Unavailable",
CLK1_TPM_group %in% c("High CLK1 TPM", "Low CLK1 TPM")
),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))
ggsave(file.path(plot_dir, paste0("forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
forest_cluster_clk1_efs,
width = 9, height = 4, units = "in",
device = "pdf")
add_model_cluster_os <- fit_save_model(cluster_df %>%
dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
forest_cluster_clk1_os,
width = 9, height = 4, units = "in",
device = "pdf")
#Assess EFS and OS by CLK1 PSI group in multivariate models and generate forest plots
add_model_cluster_efs <- fit_save_model(cluster_df %>%
dplyr::filter(extent_of_tumor_resection != "Unavailable",
CLK1_PSI_group %in% c("High CLK1 PSI", "Low CLK1 PSI")),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
forest_cluster_clk1_efs,
width = 9, height = 4, units = "in",
device = "pdf")
add_model_cluster_os <- fit_save_model(cluster_df %>%
dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
forest_cluster_clk1_os,
width = 9, height = 4, units = "in",
device = "pdf")
# Assess EFS and OS by CLK1 ex 4 TPM in multivariate models and generate forest plots
add_model_cluster_efs <- fit_save_model(cluster_df %>%
dplyr::filter(extent_of_tumor_resection != "Unavailable"),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
forest_cluster_clk1_efs,
width = 9, height = 4, units = "in",
device = "pdf")
add_model_cluster_os <- fit_save_model(cluster_df %>%
dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
#%>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
forest_cluster_clk1_os,
width = 9, height = 4, units = "in",
device = "pdf")
# Assess EFS and OS by CLK1 ex 4 PSI in multivariate models and generate forest plots
add_model_cluster_efs <- fit_save_model(cluster_df %>%
dplyr::filter(extent_of_tumor_resection != "Unavailable"),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma"))),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
"multivariate",
years_col = "EFS_years",
status_col = "EFS_status")
forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi.pdf")),
forest_cluster_clk1_efs,
width = 9, height = 4, units = "in",
device = "pdf")
add_model_cluster_os <- fit_save_model(cluster_df %>%
dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
# %>%
# dplyr::mutate(plot_group = fct_relevel(plot_group,
# c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
# "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
# "Other CNS embryonal tumor", "Low-grade glioma")), ),
terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
"multivariate",
years_col = "OS_years",
status_col = "OS_status")
forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))
ggsave(file.path(plot_dir, paste0( "forest_add_OS_", each, "_histology_resection_clk1_psi.pdf")),
forest_cluster_clk1_os,
width = 9, height = 4, units = "in",
device = "pdf")
}Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 2 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 2,6 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 2 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 2 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,4,5 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
one or more coefficients may be infinite`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,4,5,6,7,8,9,10 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 6,7,8,9 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 6,7,9 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 6,7,8,9 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 6,7,9 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,4,5,6,7,8 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,4,5,6,7,8 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
one or more coefficients may be infinite`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,3,4,5,7,8,9,10,11 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 5,6,7 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 5,6,7 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 5,6,7 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,4,5 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,6,8 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,6,8 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,5,7,8 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 5,6 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_TPM_group with kap.meier
Testing model: survival::Surv(OS_days, OS_status) ~ CLK1_PSI_group with kap.meier
Testing model: survival::Surv(EFS_days, EFS_status) ~ CLK1_PSI_group with kap.meierIgnoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• fill : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.Ignoring unknown labels:
• colour : ""Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.
Warning: No shared levels found between `names(values)` of the manual scale and the data's fill values.
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4 ; coefficient may be infinite. `height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 1,2,4,6 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,6 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,6 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,6 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Loglik converged before variable 4,6 ; coefficient may be infinite.
Warning in scale_x_log10(labels = function(x) format(x, scientific = FALSE)) :
log-10 transformation introduced infinite values.`height` was translated to `width`.Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_text()`).
Print session info
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gtools_3.9.5 survminer_0.4.9 patchwork_1.2.0 ggpubr_0.6.0 survival_3.7-0 lubridate_1.9.4 forcats_1.0.1 stringr_1.6.0 dplyr_1.1.4
[10] purrr_1.2.0 readr_2.1.6 tidyr_1.3.1 tibble_3.3.0 ggplot2_4.0.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 farver_2.1.2 S7_0.2.1 fastmap_1.2.0 digest_0.6.39 timechange_0.3.0 lifecycle_1.0.4
[8] magrittr_2.0.4 compiler_4.4.0 rlang_1.1.6 sass_0.4.10 tools_4.4.0 yaml_2.3.10 data.table_1.17.8
[15] knitr_1.50 ggsignif_0.6.4 labeling_0.4.3 bit_4.6.0 xml2_1.5.0 RColorBrewer_1.1-3 abind_1.4-5
[22] withr_3.0.2 grid_4.4.0 xtable_1.8-4 colorspace_2.1-2 scales_1.4.0 cli_3.6.5 rmarkdown_2.30
[29] crayon_1.5.3 ragg_1.5.0 generics_0.1.4 rstudioapi_0.17.1 km.ci_0.5-6 tzdb_0.5.0 commonmark_2.0.0
[36] cachem_1.1.0 splines_4.4.0 survMisc_0.5.6 vctrs_0.6.5 Matrix_1.7-4 jsonlite_2.0.0 carData_3.0-5
[43] car_3.1-2 colorblindr_0.1.0 hms_1.1.4 bit64_4.6.0-1 rstatix_0.7.2 systemfonts_1.3.1 jquerylib_0.1.4
[50] glue_1.8.0 ggtext_0.1.2 cowplot_1.1.3 stringi_1.8.7 gtable_0.3.6 pillar_1.11.1 htmltools_0.5.8.1
[57] R6_2.6.1 KMsurv_0.1-5 textshaping_1.0.4 rprojroot_2.1.1 vroom_1.6.6 evaluate_1.0.5 lattice_0.22-7
[64] markdown_1.13 backports_1.5.0 gridtext_0.1.5 broom_1.0.10 bslib_0.9.0 Rcpp_1.1.0 gridExtra_2.3
[71] xfun_0.54 zoo_1.8-12 pkgconfig_2.0.3 ---
title: "Run splicing cluster assignment, splicing burden, gsva, CLK1"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: Ryan Corbett, adapted by Jo Lynne Rokita for CLK1
date: 2024
params:
  plot_ci: TRUE
---

**Purpose:** 

Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) 2) KEGG Spliceosome GSVA scores or 3) CLK1 exon 4 TPM as a predictor

## Usage 

Uses a wrapper function (`survival_analysis`) from utils folder. 

## Setup

#### Packages and functions

Load packages, set directory paths and call setup script

```{r}
library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)

root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))

data_dir <- file.path(root_dir, "data")
analysis_dir <- file.path(root_dir, "analyses", "survival")
input_dir <- file.path(analysis_dir, "results")
results_dir <- file.path(analysis_dir, "results")
plot_dir <- file.path(analysis_dir, "plots")

# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
  dir.create(results_dir, recursive = TRUE)
}

source(file.path(analysis_dir, "util", "survival_models.R"))
```

Set metadata and cluster assignment file paths

```{r set paths}
metadata_file <- file.path(input_dir, "splicing_indices_with_survival.tsv")

cluster_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "sample-cluster-metadata-top-5000-events-stranded.tsv")

kegg_scores_stranded_file <- file.path(root_dir, "analyses",
                          "sample-psi-clustering", "results",
                          "gsva_output_stranded.tsv")

tpm_file <- file.path(data_dir, "rna-isoform-expression-rsem-tpm.rds")
clk1_psi_file <- file.path(root_dir, 
                           "analyses", 
                           "CLK1-splicing_correlations", 
                           "results", 
                           "clk1-exon4-psi.tsv")
```

Wrangle data 
Add cluster assignment and spliceosome gsva scores to `metadata` and define column `lgg_group` (LGG or non_LGG)

```{r}
metadata <- read_tsv(metadata_file)

clusters <- read_tsv(cluster_file) %>%
  dplyr::rename(Kids_First_Biospecimen_ID = sample_id)

clk1_psi <- read_tsv(clk1_psi_file) %>%
  dplyr::rename(CLK1_ex4_PSI = PSI) %>%
  select(-plot_group)

gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
  dplyr::filter(geneset == "KEGG_SPLICEOSOME") %>%
  dplyr::rename(spliceosome_gsva_score = score)

all_clk4_transcr_counts <- readRDS(tpm_file) %>%
  filter(grepl("^CLK1", gene_symbol)) %>%
  mutate(
    transcript_id = case_when(
      transcript_id %in% c("ENST00000321356.9", "ENST00000434813.3", "ENST00000409403.6") ~ "Exon 4",
#      transcript_id == "ENST00000321356.9" ~ "Exon 4",
      TRUE ~ "Other"
    )
  ) %>%
  group_by(transcript_id) %>%
  summarise(across(starts_with("BS"), sum, na.rm = TRUE)) %>%
  pivot_longer(cols = -transcript_id, names_to = "Kids_First_Biospecimen_ID", values_to = "CLK1_Ex4_TPM") %>%
  filter(transcript_id == "Exon 4") %>%
  inner_join(clusters, by = "Kids_First_Biospecimen_ID") %>%
  left_join(clk1_psi)

# how many clusters?
n_clust <- length(unique(clusters$cluster))

metadata <- metadata %>%
  right_join(all_clk4_transcr_counts %>% dplyr::select(Kids_First_Biospecimen_ID,
                                       cluster, CLK1_Ex4_TPM, CLK1_ex4_PSI)) %>%
  left_join(gsva_scores %>% dplyr::select(sample_id,
                                          spliceosome_gsva_score),
            by = c("Kids_First_Biospecimen_ID" = "sample_id")) %>% 
  dplyr::mutate(cluster = glue::glue("Cluster {cluster}")) %>%
  dplyr::mutate(cluster = fct_relevel(cluster,
                                               paste0("Cluster ", 1:n_clust))) %>%
  dplyr::mutate(lgg_group = case_when(
    plot_group == "Low-grade glioma" ~ "LGG",
    TRUE ~ "non-LGG"
  )) %>%
  dplyr::mutate(SBI = SI_Total * 10) %>%
  dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)
```

Generate coxph models including extent of tumor resection, lgg group, cluster assignment, SBI, and CLK1 exon 4 TPM as covariates

```{r}
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")
```
repeat analysis with CLK1 exon 4 TPM alone

```{r}
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


```


repeat analysis with CLK1 exon 4 PSI

```{r}
add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
       forest_os,
       width = 10, height = 6, units = "in",
       device = "pdf")

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")
```


Interaction with GSVA, SBI, CLK1

```{r Interaction models with GSVA, SBI, CLK1}
models <- c("spliceosome_gsva_score", "SBI", "CLK1_Ex4_TPM", "CLK1_ex4_PSI")
# by cluster
for (each in models) {
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")
}

## clk1 x age
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")

models2 <- c("SBI", "CLK1_Ex4_TPM")
for (each in models2) {
  #### by spliceosome_gsva_score
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+spliceosome_gsva_score*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")
}
  


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


```



Filter for clusters

```{r}
cluster_list <- unique(metadata$cluster)

for (each in cluster_list) {
cluster_df <- metadata %>%
  dplyr::filter(cluster == each,
                !is.na(EFS_days)) %>%
  dplyr::mutate(CLK1_TPM_group = case_when(
      CLK1_Ex4_TPM > summary(CLK1_Ex4_TPM)["3rd Qu."] ~ "High CLK1 TPM",
      CLK1_Ex4_TPM < summary(CLK1_Ex4_TPM)["1st Qu."] ~ "Low CLK1 TPM",
      TRUE ~ NA_character_),
      CLK1_PSI_group = case_when(CLK1_ex4_PSI > summary(CLK1_ex4_PSI)["3rd Qu."] ~ "High CLK1 PSI",
      CLK1_ex4_PSI < summary(CLK1_ex4_PSI)["1st Qu."] ~ "Low CLK1 PSI",
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(CLK1_TPM_group = fct_relevel(CLK1_TPM_group,
                                                 c("Low CLK1 TPM", "High CLK1 TPM")),
                CLK1_PSI_group = fct_relevel(CLK1_PSI_group,
                                                 c("Low CLK1 PSI", "High CLK1 PSI")))

safe_each <- gsub("[^A-Za-z0-9_-]+", "_", each)

# Generate KM models with `CLK1_TPM_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_tpm_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = "CLK1_TPM_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

readr::write_rds(cluster_clk_tpm_kap_os,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_tpm_group.RDS")))

cluster_clk_tpm_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = "CLK1_TPM_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

readr::write_rds(cluster_clk_tpm_kap_efs,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_tpm_group.RDS")))

# Generate KM models with `CLK1_PSI_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_psi_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = "CLK1_PSI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "OS_days",
  status_col = "OS_status"
)

readr::write_rds(cluster_clk_psi_kap_os,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_OS_clk1_psi_group.RDS")))

cluster_clk_psi_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = "CLK1_PSI_group",
  test = "kap.meier",
  metadata_sample_col = "Kids_First_Biospecimen_ID",
  days_col = "EFS_days",
  status_col = "EFS_status"
)

readr::write_rds(cluster_clk_psi_kap_efs,
                 file.path(results_dir, paste0( "logrank_", safe_each, "_EFS_clk1_psi_group.RDS")))

# Generate cluster KM SI_group plots

km_cluster_clk_tpm_os_plot <- plotKM(model = cluster_clk_tpm_kap_os,
                    variable = "CLK1_TPM_group",
                    combined = F, 
                    title = paste0(each, ", overall survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0("km_", safe_each, "_OS_clk1_tpm_group.pdf")),
       km_cluster_clk_tpm_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_tpm_efs_plot <- plotKM(model = cluster_clk_tpm_kap_efs,
                    variable = "CLK1_TPM_group",
                    combined = F, 
                    title = paste0(each, ", event-free survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0( "km_", each, "_EFS_clk1_tpm_group.pdf")), 
       km_cluster_clk1_tpm_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_psi_os_plot <- plotKM(model = cluster_clk_psi_kap_os,
                    variable = "CLK1_PSI_group",
                    combined = F, 
                    title = paste0(each, ", overall survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0( "km_", each, "_OS_clk1_psi_group.pdf")),
       km_cluster_clk1_psi_os_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

km_cluster_clk1_psi_efs_plot <- plotKM(model = cluster_clk_psi_kap_efs,
                    variable = "CLK1_PSI_group",
                    combined = F, 
                    title = paste0(each, ", event-free survival"),
                    p_pos = "topright")

ggsave(file.path(plot_dir, paste0("km_", each, "_EFS_clk1_psi_group.pdf")), 
       km_cluster_clk1_psi_efs_plot,
       width = 8, height = 5, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 TPM group in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    CLK1_TPM_group %in% c("High CLK1 TPM", "Low CLK1 TPM")
                                                    ),
                                      # %>%
                                     # dplyr::mutate(plot_group = fct_relevel(plot_group,
                                      #                                      c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                       #                                       "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                        #                                      "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))

ggsave(file.path(plot_dir, paste0("forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
                                    # %>%
                              #      dplyr::mutate(plot_group = fct_relevel(plot_group,
                               #                                             c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                #                                              "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                 #                                             "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_TPM_group",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm_group.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

#Assess EFS and OS by CLK1 PSI group in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable",
                                                    CLK1_PSI_group %in% c("High CLK1 PSI", "Low CLK1 PSI")),
                                      # %>%
                                     # dplyr::mutate(plot_group = fct_relevel(plot_group,
                                      #                                      c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                       #                                       "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                        #                                      "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")), 
                                    # %>%
                                  #  dplyr::mutate(plot_group = fct_relevel(plot_group,
                                   #                                         c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                    #                                          "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                     #                                         "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_PSI_group",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi_group.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_psi_group.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 ex 4 TPM in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable"), 
                                   #   %>%
                                    #  dplyr::mutate(plot_group = fct_relevel(plot_group,
                                     #                                       c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                      #                                        "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                       #                                       "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")), 
                                    #%>%
                                 #   dplyr::mutate(plot_group = fct_relevel(plot_group,
                                  #                                          c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                   #                                           "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                    #                                          "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_Ex4_TPM",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_tpm.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", safe_each, "_histology_resection_clk1_tpm.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")

# Assess EFS and OS by CLK1 ex 4 PSI in multivariate models and generate forest plots

add_model_cluster_efs <- fit_save_model(cluster_df %>% 
                                      dplyr::filter(extent_of_tumor_resection != "Unavailable"),
                                      # %>%
                                 #     dplyr::mutate(plot_group = fct_relevel(plot_group,
                                  #                                          c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                   #                                           "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                    #                                          "Other CNS embryonal tumor", "Low-grade glioma"))),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
                               file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_cluster_clk1_efs <- plotForest(readRDS(file.path(results_dir, paste0( "cox_EFS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_EFS_", safe_each, "_histology_resection_clk1_psi.pdf")),
       forest_cluster_clk1_efs,
       width = 9, height = 4, units = "in",
       device = "pdf")

add_model_cluster_os <- fit_save_model(cluster_df %>% 
                                    dplyr::filter(!extent_of_tumor_resection %in% c("Not Reported", "Unavailable")),
                                    # %>%
                               #     dplyr::mutate(plot_group = fct_relevel(plot_group,
                                #                                            c("Other high-grade glioma", "Atypical Teratoid Rhabdoid Tumor",
                                 #                                             "DIPG or DMG", "Ependymoma", "Mesenchymal tumor",
                                  #                                            "Other CNS embryonal tumor", "Low-grade glioma")),                                 ),
                              terms = "extent_of_tumor_resection+age_at_diagnosis_years+plot_group+CLK1_ex4_PSI",
                               file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS")),
                               "multivariate",
                               years_col = "OS_years",
                               status_col = "OS_status")

forest_cluster_clk1_os <- plotForest(readRDS(file.path(results_dir, paste0( "cox_OS_additive_terms_subtype_", safe_each, "_clk1_psi.RDS"))))

ggsave(file.path(plot_dir, paste0( "forest_add_OS_", each, "_histology_resection_clk1_psi.pdf")),
       forest_cluster_clk1_os,
       width = 9, height = 4, units = "in",
       device = "pdf")
}
```


Print session info

```{r}
sessionInfo()
```